On the other hand, numerous algorithms have been proposed since decades from the machine learning community to leverage statistical learning methods for solving difficult combinatorial search problems (see the recent survey of [1]} on this topic). These attempts have been given a new lease of life, with the emergence of deep learning techniques for combinatorial optimization problems [2]}, [3]}, inspired from the great success of the AlphaZero algorithm for combinatorial games [4]}. In particular, some recent works have applied reinforcement learning and deep learning to solve graph coloring problems [5]}, [6]}. Nevertheless, these studies rarely exploit specific knowledge of the problem, which make these approaches more general but may limit their performance. Indeed, the results obtained by this type of approach are for the moment far from the results obtained by state of the art algorithms on graph coloring problems such as hybrid algorithms [7]}, [8]}, [9]} and simulated annealing algorithms [10]}. We can mention however new works which are trying to pair efficient local search algorithms and machine learning techniques [11]}, [12]} with promising results for graph coloring problems.
